Computing frequent graph patterns from semistructured data

Vanetik, Natalia; Gudes, Ehud; Shimony, Solomon Eyal

doi:10.1109/icdm.2002.1183988

Cited by 100 publications

(109 citation statements)

References 7 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…Similar type of approaches have been used earlier in the context of vertical algorithms for the graph-transaction setting [42,44]. All of these share the same idea, which avoids Downloaded 03/22/19 to 52.247.194.177.…”

Section: Generating Parent Identificationmentioning

confidence: 91%

“…Both G 7 and G 6 are subgraphs of G. Although the smaller subgraph G 6 has only one non-identical embedding, the larger G 7 has six non-identical embeddings. On the other hand, if we determine the frequency of each subgraph by counting the maximum number of its edge-disjoint embeddings, then the resulting frequency is downward closed [42].…”

Section: Discovering Frequent Patterns In a Single Graph: Problem Defmentioning

confidence: 99%

“…For this reason, our problem formulations utilize edge-disjoint embeddings. Given this, one way of formulating the frequent subgraph discovery problem for the single-graph setting as follows [42]: DEFINITION 3.1. (EXACT DISCOVERY PROBLEM) Given an input graph G which is undirected and labeled, and a parameter f , find all connected undirected labeled subgraphs that have at least f edge-disjoint embeddings in G.…”

Section: Discovering Frequent Patterns In a Single Graph: Problem Defmentioning

confidence: 99%

“…However, developing algorithms that are capable of finding patterns in the single-graph setting has received much less attention, despite the fact that this problem setting is more generic and applicable to a wider range of datasets and application domains than the other. Moreover, existing algorithms that are guaranteed to find all frequent patterns [13,42] or algorithms that are heuristic, such as GBI [47] and SUBDUE [19] which tend to miss a large number of frequent patterns, are computationally expensive and do not scale to large datasets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Finding Frequent Patterns in a Large Sparse Graph

Kuramochi

Karypis

2004

Proceedings of the 2004 SIAM International Conference on Data Mining

134

180

View full text Add to dashboard Cite

This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edge-disjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edge-disjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation on real datasets from various domains show that both algorithms achieve good performance, scale well to sparse input graphs with more than 100,000 vertices, and significantly outperform a previously developed algorithm.

show abstract

Section: Generating Parent Identificationmentioning

confidence: 91%

Section: Discovering Frequent Patterns In a Single Graph: Problem Defmentioning

confidence: 99%

Section: Discovering Frequent Patterns In a Single Graph: Problem Defmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finding Frequent Patterns in a Large Sparse Graph

Kuramochi

Karypis

2004

Proceedings of the 2004 SIAM International Conference on Data Mining

134

180

View full text Add to dashboard Cite

show abstract

“…Semantic relations between information resources: metrics and algorithms for semantic distance in graphs [11,12]. Clustering [13,14] and graph pattern mining algorithms [15] to reveal regularities in RDF data.…”

Section: Introductionmentioning

confidence: 99%

Bipartite Graphs as Intermediate Model for RDF

Hayes

Gutiérrez

2004

Lecture Notes in Computer Science

109

113

View full text Add to dashboard Cite

Abstract. RDF Graphs are sets of assertions in the form of subjectpredicate-object triples of information resources. Although for simple examples they can be understood intuitively as directed labeled graphs, this representation does not scale well for more complex cases, particularly regarding the central notion of connectivity of resources. We argue in this paper that there is need for an intermediate representation of RDF to enable the application of well-established methods from graph theory. We introduce the concept of RDF Bipartite Graph and show its advantages as intermediate model between the abstract triple syntax and data structures used by applications. In the light of this model we explore the issues of transformation costs, data/schemastructure, and the notion of RDF connectivity.

show abstract

Discriminative frequent subgraph mining with optimality guarantees

Thoma

Cheng

Gretton

et al. 2010

Statistical Analysis

View full text Add to dashboard Cite

The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near-optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining.

show abstract

Computing frequent graph patterns from semistructured data

Cited by 100 publications

References 7 publications

Finding Frequent Patterns in a Large Sparse Graph

Finding Frequent Patterns in a Large Sparse Graph

Bipartite Graphs as Intermediate Model for RDF

Discriminative frequent subgraph mining with optimality guarantees

Contact Info

Product

Resources

About